Examining the Impact of Case Management in Vancouver’s Downtown Community Court: A Quasi- Experimental Design
Bibliographic record
Abstract
Background: Problem solving courts (PSC) have been implemented internationally, with a common objective to prevent reoffending by addressing criminogenic needs and strengthening social determinants of health. There has been no empirical research on the effectiveness of community courts, which are a form of PSC designed to harness community resources and inter-disciplinary expertise to reduce recidivism in a geographic catchment area. Method: We used the propensity score matching method to examine the effectiveness of Vancouver’s Downtown Community Court (DCC). We focused on the subset of DCC participants who were identified as having the highest criminogenic risk and were assigned to a case management team (CMT). A comparison group was derived using one-to-one matching on a large array variables including static and dynamic criminogenic factors, geography, and time. Reductions in offences (one year pre minus one year post) were compared between CMT and comparison groups. Results: Compared to other DCC offenders, those triaged to CMT (9.5 % of the DCC population) had significantly higher levels of healthcare, social service use, and justice system involvement over the ten years prior to the index offence. Compared to matched offenders who received traditional court outcomes, those assigned to CMT (n = 249) exhibited significantly greater reductions in overall offending (p,0.001), primarily comprised of significant reductions in property offences (p,0.001).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".